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Instrument-free inference under confined regressor endogeneity; derivations and applications

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  • Kiviet, Jan

Abstract

A fully-fledged alternative to Two-Stage Least-Squares (TSLS) inference is developed for general linear models with endogenous regressors. This alternative approach does not require the adoption of external instrumental variables. It generalizes earlier results which basically assumed all variables in the model to be normally distributed and their observational units to be stochastically independent. Now the chosen underlying framework corresponds completely to that of most empirical cross-section or time-series studies using TSLS. This enables revealing empirically relevant replication studies, also because the new technique allows testing the earlier untestable exclusion restrictions adopted when applying TSLS. For three illustrative case studies a new perspective on their empirical findings results. The new technique is computationally not very demanding. It involves scanning least-squares-based results over all compatible values of the nuisance parameters established by the correlations between regressors and disturbances.

Suggested Citation

  • Kiviet, Jan, 2019. "Instrument-free inference under confined regressor endogeneity; derivations and applications," MPRA Paper 96839, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:96839
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    Cited by:

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    3. Ahsan, Nazmul & Emran, M. Shahe & Shilpi, Forhad, 2021. "Complementarities and Intergenerational Educational Mobility: Theory and Evidence from Indonesia," MPRA Paper 111125, University Library of Munich, Germany.
    4. Dovì, Max-Sebastian & Koester, Gerrit & Nickel, Christiane, 2021. "Addressing the endogeneity of slack in Phillips Curves," Working Paper Series 2619, European Central Bank.

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    More about this item

    Keywords

    endogeneity robust inference; instrument validity tests; replication studies; sensitivity analysis; two-stage least-squares.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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